{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from collections import namedtuple\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import PIL\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torchvision.datasets as dset\n",
    "from torch.utils.data.sampler import SubsetRandomSampler\n",
    "\n",
    "from torchvision import transforms\n",
    "\n",
    "from support import compute_loss_accuracy, accuracy_number, compute_error_model, Flattener"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "Загружаем тестовые данные"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "test_data  = dset.MNIST('./data/', train=False,\n",
    "                    transform=transforms.Compose([\n",
    "                           transforms.ToTensor(),\n",
    "                           transforms.Normalize(mean=[0.43],\n",
    "                                               std=[0.20])\n",
    "                       ]))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Выбираем на каком железе тестировать (CPU vs GPU)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU\n"
     ]
    }
   ],
   "source": [
    "if torch.cuda.is_available():\n",
    "    device = torch.device('cuda:0')\n",
    "    print(\"CUDA\")\n",
    "else:\n",
    "    device = torch.device('cpu')\n",
    "    print(\"CPU\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Загружаем тренированную модель"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "Sequential(\n  (0): Conv2d(1, 8, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2))\n  (1): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n  (2): ReLU(inplace=True)\n  (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n  (4): Conv2d(8, 16, kernel_size=(3, 3), stride=(1, 1))\n  (5): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n  (6): ReLU(inplace=True)\n  (7): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n  (8): Flattener()\n  (9): Linear(in_features=576, out_features=10, bias=True)\n)"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "load_model = torch.load(\"./model_v3.pt\", device)\n",
    "load_model.eval()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Тестируем!"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.029164, accuracy: 0.991100\n"
     ]
    }
   ],
   "source": [
    "test_loader = torch.utils.data.DataLoader(test_data, batch_size=64)\n",
    "loss = nn.CrossEntropyLoss().type(torch.FloatTensor)\n",
    "test_loss, test_accuracy = compute_loss_accuracy(load_model, test_loader, loss, device)\n",
    "print(\"Test loss: %f, accuracy: %f\" % (test_loss, test_accuracy))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Точность для каждой цифры"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number: 0, accuracy: 0.993878\n",
      "Number: 1, accuracy: 0.998238\n",
      "Number: 2, accuracy: 0.992248\n",
      "Number: 3, accuracy: 0.991089\n",
      "Number: 4, accuracy: 0.993890\n",
      "Number: 5, accuracy: 0.991031\n",
      "Number: 6, accuracy: 0.990605\n",
      "Number: 7, accuracy: 0.986381\n",
      "Number: 8, accuracy: 0.987680\n",
      "Number: 9, accuracy: 0.985134\n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "  accuracy = accuracy_number(load_model, test_loader, i, device)\n",
    "  print(\"Number: %i, accuracy: %f\" % (i, accuracy))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Покажем рандомные предсказания"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1152x576 with 24 Axes>",
      "image/png": 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AAIBgNCIBAAAAAMFoRAIAAAAAgtXNJbMxhll4qkkURWbFucJQj9WHeiwP1GN5KFQ9UofVh2uxPFCP5YF6LA+h9ciTSAAAAABAMBqRAAAAAIBgNCIBAAAAAMFoRAIAAAAAgtGIBAAAAAAEoxEJAAAAAAhGIxIAAAAAEIxGJAAAAAAgGI1IAAAAAECwutVdAAClrWXLljbu0aOHl9a9e3cbb7bZZjbu16+fl++aa64pUukAoPZp1qyZje+44w4v7aijjrLxmDFjbLzbbrt5+RYsWFCk0gEoBzyJBAAAAAAEoxEJAAAAAAhGIxIAAAAAEMxEURSe2ZjwzDVA48aNbfzcc895aZ07d7bx119/7aW5ee+66y4bT548ucAlDBdFkSnUsUqhHt26u/jii23crVs3L99f//pXG/fs2dNLe+CBB4pTuEoo1XrcaKONvO3TTjvNxieddJKNGzZsmNfx58+fb+P33nvPSzv00EMrzFedSrUe4StUPVKH1YdrMXb22Wd723379rVx06ZNM+7nft5Jj03/z3/+U6DSrRj1WB7KpR6vvPJKG3fs2NHG9957r5dv2LBhNl64cGHQsbfddltvO/3ZNZO//e1vNh49erSX1rt3bxt///33QcfLJrQeeRIJAAAAAAhGIxIAAAAAEKzsurOuvvrqNh4yZIiNu3TpktfxZs2aZeMddtjBS/v222/zOmY+yqWLQKg+ffrY+KqrrgraZ/Hixd72Y489ZuPbbrvNxmPHjq1k6fJXSvXoLs+RXpKjTZs2BX0tY5b/WtL3pFdeecXGbneO2bNnF7QMuSileiw0t6t5ixYtvLQZM2bYeM6cOXkdf4sttrDxf//7Xy/NvaaPPvpoG+fyPuaiO2vpq23XYr169Wz89ttv23iTTTbx8r311ls2fvbZZ70097PR0qVLbbxkyRIvn5tWbKVUj40aNbLxfffd56UddthhbjlsnL5HDRw40MZuXaW5nzPTXRhrolKqx2x23XVXG0+YMMHGU6dOzbhP+/btbbzuuut6aSeffLKNt99+ey9trbXWyreY1syZMyssR7byZkN3VgAAAABAwdGIBAAAAAAEK/nurG53AUnq1auXjd1ZlBYsWODlc7scfPPNN17aKqusYuP999/fxnfccYeXL919spjKpYtAJiut5H+fMWrUKBt36NCh0sefN2+ejffdd18vLT0baDHV5Hp0uwdKfjedunXrZtzv119/tfGTTz7ppd166602dq+rSy+91Mt38MEH2zjbPcm9bocOHZoxX7HV5HoshI033tjb7tSpk43POussG2+55ZZevs8//9zGhx9+uJeWvs9m8uWXX9rYnX057YgjjrDxU089FXTstJranfWoo47ytt2uiulhFO7sgO59rrYo92sxzb0Hup9JxowZ4+Vzu/7XlFmtsymlerz77rtt7M6KWQzuTJufffZZxnzTp0+38ZlnnumlLVq0qPAFy6CU6jEf6c+qbrfXp59+2sarrrqql+9f//qXjdOrRYwfPz7otW+44QYbu7PWp7388ss2dtswuaA7KwAAAACg4GhEAgAAAACC0YgEAAAAAATLPNCpRKT7+7rjIF0HHnigtz1ixIig43/99dcZ09Zee20bH3fccV7aBhtsUOE+L774orf92muv2fi3334LKlM52n333b3tQoyDdLlTcqf/FqpyTGRN07p1axunxym64yB/+uknL80dj+iOy3HHs2WT7s9/3nnn2Ti9pEudOnVsfMwxx1RYBuRnzTXXtLE7nqNt27ZeviZNmgQdz12eY7fddvPSxo0bZ2N3LHu6vrMtH+MuOfDmm28GlakUpX8nmd5PJH95gK5du9o43yVWULM0bNjQ23bvnc2aNbNxt27dqqxMtV27du0yprnvlU888YSNjz/+eC9ful4zcZeKSC8bkUl6yYhjjz3WxtwXKid9L15//fVt3LRp04K+VvpzcPpzciY77rhjQcuRDU8iAQAAAADBaEQCAAAAAIKV/BIfffr08bbT3YCWcbueSn/unpePk046ycYDBgzI6xgXXXSRja+77rqM+cpx2mS3u+Qrr7zipaW7wmUyZcoUGz/yyCNeWvpvY5n00ix77bWXjd95552g181XTavHCy+80Mb9+/f30twlPm677TYvzV3KodBuvvlmb9tdUsK9bt2ptaXwJSQKoabVYw6v5W0PGTLExu7SAcXgdp3db7/9bDx48ODgY7jDFU499dRKl6mmLvGxzjrreNvuvS39d+/65JNPbHzttdd6ae7QjGxLBbhatmzpbbvd33fYYQcbp5diyaeLe75K9VoM9fzzz3vb7hAe9zPIoEGDqqxMxVBK9eguZTVp0iQv7cEHH7Sxe51tu+22Xr569erZ2L2W0u/D6aUi8uGWKd2tttBKqR5rInd5p/TnrjXWWCPoGHvssYeNQ4fupbHEBwAAAACg4GhEAgAAAACC0YgEAAAAAAQr+SU+ttlmm4xp7liCX375peCv/d1331UYS9KGG25Y4T7jx4/3tt0xSbXNKaecYuPQMZCTJ0/2tvfdd18bp3+3CxcutPFll11m45VXXtnL5y4vUewxkTXZr7/+6m337dvXxj/++GOVlSN9LbmaN29u48aNG1dFccqKOw5HCh8H+fHHH9vYHbM6Y8YML9+ee+5p4969e3tpL7zwgo3dMUDZ/Pzzz962O7annE2dOtXbdsdtu0t6SNJOO+1k46233trGjz/+uJfPXUIqfR/NJL20i3v9ZeMueeVOgS/9uU7xZ+77mhtLfr3mMp4YhdO9e/ec9/noo48yprmff9JjLNPLLeXjiy++qPQxkJv0e+3OO+9s4/TSHW4dH3300TZOz2HgSs9nc+ONN9r47bffzq2wlcCTSAAAAABAMBqRAAAAAIBgJdmd1e1i83//938Z87ndp5YsWVLp1z344IO9bbeLZKbuq5L/aPnYY4/10twlKmqbrl27BuVzu8ylp/V3p61Pc6fKPuSQQ2zcrl07L5+7/EuDBg28NLdLbDlyu3y/8cYbXlpVdmHNR7pL0YcfflhNJSkdv//+u7ftLh3hdqNJ53O7nmf7PbtdLbt06eKlhXZhdV/bXd5FkkaPHh10jHLj/k4WLFiQMd+iRYts7C73IfndqTbZZJOg100PEXCni3fvD6effrqXr2HDhjZO37P79esX9Nq12fbbb2/jlVbyv+t/4oknbFyIzzWoGkceeaS3fckll9jYXSInWxfGbNxrP32NpZeKwIrVqVPHxltuuaWN0587WrRoUeH+m222mbe9yiqr2Dj9GTSTefPmedtXXHGFjdNDEtxlZ6oSTyIBAAAAAMFoRAIAAAAAgpVkd1Z3Jqv0bHGzZs2ycb4zFLndrm666SYbpx9Bu90O3C4mkvToo4/a+MUXX7Tx0qVL8ypTubj99ttt3KlTp6B93NnpPvvss4KXabvttrNxultyuc9q9vnnn1d3Ef5k5MiR3vbs2bNtvPrqq9vYnYkSYdIzuqVnAlwm3aXK7YrjqlvXfwu55pprbJxt5mxXeubsvffe28burLC12brrrmvjzp07Z8znvv+l76/pWalD/PHHH9622632pJNOyrife81utdVWOb9ubdSxY0cbn3322TYeM2aMl+/ll1+usjKhcNxZrSVprbXWKujx3VnNr7vuuoIeuza6/PLLbezOVF8M//vf/2zsdj1+8803vXw18fMaTyIBAAAAAMFoRAIAAAAAgtGIBAAAAAAEK8kxke7012kTJ0608TfffJMx36qrrmrj9HTIJ598so3dqcr/85//ePncJT5ee+21zAWuxdxpkiV/XGm2MTrDhg2zcSH6gb/zzjsVliEt/bdV7mMia6J0fc+fP9/G7phIVN7jjz9u44suusjG6bGOgwYNsvE999xj4wMPPNDLt+uuuwa97s8//2zjbt26eWmMg/wzd4xqeskH1xprrGFjd0kPyb8HFsKhhx6aMe2uu+6ycbHHE5WLLbbYwsbufW7UqFFePncph0aNGtl4zz339PK516L7uUjyPxsNHz7cxrV9zoZS1qZNGxvfeuutXtqFF15o43JftqxQevbsWWWv1atXLxun54So6XgSCQAAAAAIRiMSAAAAABCsJLuzzp07N+d9dtppJ2/7kUcesXHr1q29tPHjx9u4f//+Nn7ppZe8fOmp6fFnm222mbftTmOezfPPP2/jQnSxOfPMM218/PHHe2lul2V3eRdJGjx4cKVfG7k5/fTTve111lmnwnzpZSiQu6+//trGN9xwg43PPfdcL9/GG29sY3fZo1z8+uuvNt5///1t/OGHH+Z1PPyZ2w35oIMO8tIK3Z3V7frv/h1JdGEN8Ze//MXbvuKKKyrM99VXX3nb7pIpzzzzjI3Ty1O5XcabNWvmpbn3Tre77HHHHeflmzJlSoVlQu7OOeccbzvTskmnnnqqtx26VJJ77Z9xxhlemvuZ+ZJLLgk6Xm3nthHOP/98G//www9evmnTptnYXQJp88039/K5Q+jSBg4caGO3W/tvv/2WQ4mrB08iAQAAAADBaEQCAAAAAIKVZHdWtztHmttF5Nhjj7XxHXfc4eVr3LixjdMzWbndtdxH1chd9+7dg/KNGzfO237qqaeKURyUoCiKcvp5Rfbaay8b//Wvf7Xx+++/7+X76KOPcixdafvjjz9s3KdPHxuvueaaXr4ePXrkfOwvv/zS27755pttTBfW3CxYsMDG06dP99JatGhR4T6dOnXytt3ubm6958Lt4tW0aVMbDxkyJK/j1WbpWTIbNGhQYb5NNtnE277++utt7M7UeuWVV3r5nnzySRufffbZXpo7s647i2v6GO6skUuWLKmwfAjz2GOPBeV7+OGHvW13Nub0SgLpYQeZdO7cOSgflnO75LvdxvPtzuq+vx599NFe2kYbbWTjoUOH2ni//fbLtdhVjieRAAAAAIBgNCIBAAAAAMFoRAIAAAAAgpXkmMjhw4fb+PDDD/fS1l9/fRu7yzO408tLUocOHWycHgeV73gRxFZeeWUbu1MjZ+OO+ZHyW8Ylm2OOOcbG7pIeKF3psWDuVPXp6e6bNGliY7f+00sR1LYxkZm0a9eu0sdwx1NJ0pgxYyp9zNrK/ZvNNAYyLT3OPJ/3tUaNGnnb7tIs7hJX7hT1uXDPq169el6aO74o/f5QDrbbbjtvO/27XuaUU07xtt05G9zxke7vKy19Lb7wwgs2fuKJJ2ycXuLDXeJswoQJGY+PwslWj+n5O0LHRCJ37hjgfN67vvjiC2/bnVfAHecqSUcddZSN9957bxun5xRxxznXFDyJBAAAAAAEoxEJAAAAAAhWMt1ZV111VRufdtppQfs8/fTTNk5Pjfz5558XpmDIyu3aWp26du0alG/GjBlFLgkq4i67EdpFp23btnm9ltut75tvvsnrGOXIHRpQiO6smZYsQNVwl+PIV+vWrb3t1Vdf3cbukIP0sJKffvrJxltssYWXdsghh9jYncJ+3XXX9fJNnjzZxu4wlXKxePFib9tdssgYY+P0cgAvv/xypV/bXUbAXeaoY8eOXj53+Q+6swL5W7p0qY2vu+46L+3AAw+0sdvWSS9NOHr0aBtPmTKl0EXMC08iAQAAAADBaEQCAAAAAILRiAQAAAAABKuxYyLr1Knjbd9///023nrrrYOO8dprr9mYMZBVxx1z9swzz3hp7niYYltrrbVsvOOOO2bMN23aNBsPGjSoqGUqd25/fknq0qWLjfv06ZNxv5YtW1YY5+uzzz7ztr/99lsbX3311Tb++OOPK/1apaxVq1Y2vv32223sjslKc8eap8e7uWNb3Xu2JG255ZY2nj9/fu6FrcVmz55t4/TYtI033tjG7jiZr7/+utKvu+eee2ZMa9y4sY0vvPBCL+2MM86wcfqekOlvK70kyZdffhlczlKUXjbAXV7IXf6j2MsOZZsHwH2/5r2xdM2aNau6iwDHDz/84G27yyW598s111zTy3fyySfb+JJLLilS6XLDk0gAAAAAQDAakQAAAACAYDWqO+umm25q41tvvdVL22uvvWy8aNEiG6eXkFhppeXt4t13393GAwcOLFQxsQLuVMaTJk2qstdt1KiRtz18+HAbu9320twujVOnTi18wUrUGmusYeMNN9zQS9too41sfMIJJ9g43XUtWzdil9vFzZ3qPu3XX3+1cXqq+/vuu8/G6e7r7pIDWG7nnXe2cfPmzTPmc5cEcOv7X//6l5fP7c6aXpahXr16eZeztpszZ46NDzjgAC/N7Vbqdk38/vvvvXzuEBH3GnPfMyX/PdWdej4X7r043XXrySeftPEDDzxg43QX9PQSGOXunnvusbHbdXTffff18rm/s2Jzu0qj+u2///557ed2L68N3OWC3OXd0p8D7777bhunPyP89ttvRSrdn4fkZfp8+t1333nbd911V9HKlC+eRAIAAAAAgtGIBAAAAAAEq9burG3atPG2L7vsMhu73Vcl6ZtvvrFx3759bbzPPvt4+U488cRCFhGVNHjwYG/77LPPrjDfBhts4G1fe+21Nr700kttnO7itPbaa9v473//u5eWnjlymXQ3hRtuuKHCfLVN06ZNve0nnnjCxrvuumtRXztbd1Z3ZsoePXrY+MUXXyxqmcpRetbrTDPmut2GJemaa66pMD7iiCMyvtaQIUO8bbcekT/3vTAbt0uXJJ133nk2drsdu91hJalDhw45l+n999/3tocNG2bj2267zUtzh6Ngua+++qrCn7vvhZI/W2uxZ52fO3duUY+PP0t3L+/du7eN038Lmfz444/e9u+//175gpUQd5bqbMNj3Pe/d955x0ubPn26jQcMGGDjTz75xMuXaeZbd4ZlyW/TnHrqqRnL5Hr33Xe97WwzKVcXnkQCAAAAAILRiAQAAAAABKMRCQAAAAAIVq1jIk8//XRv+/DDD7fxH3/84aXtsssuNnbH67hjO9Lc8ZLp8V6//PJLboVFXiZMmOBtf/DBBzbefvvtbbz66qt7+dzxOy1atLBxeuyNOwY2PW1yJrfffru3ne4LX1u5Yy+k4o+DzGT+/Pne9t/+9jcbv/LKK1VdnLKSHhPZrl27CvO5SwxIUs+ePW0cOp7j+eef97azjU1BYZx55pk2vvLKK7201VZbraCv1a9fPxv379/fS6ttY7AKYcyYMTZ2ly9KL/Hx3nvv2fjZZ5+18UUXXeTlc8dP1a3rf9Q76aSTbOx+TnKX55LCr/Xaxn2v/Pbbb720119/vVLHPuaYY7ztO++8M2g/dxzkYYcd5qXVtqXLHn30URsfddRRQft07NgxY1r37t1tPHHiRC8t/XllGXcZNElaZZVVgsrhcq/vmoonkQAAAACAYDQiAQAAAADBqrU7azbpqasvvvhiGw8dOtTGTZo0yXiMdNctVL30dO7u4/ltttnGxtnqyu3eke7qEcrtYuIuJYPlNt54Y2/brbt8umLkwl12xZ1OW6ILayG5XYOzOeGEE7zt9HCATNwuVaNHjw4vGAripZdesnGmJY4k/3p75JFHMuY78sgjve2zzjrLxnPmzLEx3Vcrz+1Ketppp9n4rrvu8vK53VuzvTeOGzfOxptssomX5i6pNGLECBtfffXVXj63iy2WO+6442y82WabeWknn3yyjadNm5bxGG6XYnfoSPPmzfMqkzvUJ700RG3jduvfdtttbZxeVjAfrVu3rvQx0tzuxu7SZe5SSTUVTyIBAAAAAMFoRAIAAAAAgtGIBAAAAAAEM7lMu26MKegc7e4yDpJ07bXXFvLweuKJJ2wcOs1vTRVFkVlxrjCFrsd83XPPPTZ2xxEUyqxZs2y8ww472Dg9JXdVKqV6dMdUHXDAARnzuePn0tNaZ/Lqq69629dff72NR44cGVjC6lNK9ehyrwPpz0vm5GrBggXe9k477WTjL774olLHrgqFqseack8tNHfZLUl6/PHHbXzuuefa+KabbqqyMqWV6rUYaqWV/O/6e/ToYePdd9/dxun5ITp37mzjhx9+2Et76623bOzOF/DTTz9VpqiVUkr16I45dO95xTZ79mxvu2/fvjZ+8sknbfzzzz9XWZnSalo9HnHEETa+4oorvLRCjJHMZPr06d62O/Z8jTXW8NLOPvtsG6fruLqE1iNPIgEAAAAAwWhEAgAAAACCVWt31pVXXtnbdrtf7L///l6aO8W8O039H3/84eXr37+/jf/973/bOP1oudTUtC4CheDWqdvlQJI23XRTG5944ok2Ti814XbhmDhxopfmTo0+ZcqUSpW1UMqxHmujUq3HdNe4559/3sb77LNP0DFmzJhh41NPPdVLK4UpyV10Z80ufb91h6C499uHHnqoqor0J6V6LcJXSvVYzO6sH330kbf9ww8/2Piqq67y0mriMko1uR7/8pe/eNvNmjWzcXqJs0suucTG7hI5Q4YM8fJ98MEHNnaHh8ybN8/L575vlgK6swIAAAAACo5GJAAAAAAgWLV2Z0W4mtxFAOGox/JQLvXYsWNHG48aNSpjPrcLjzsrJ8MEYlyL1adcrsXarpTqsUuXLjZ2ZyeXpO7duwcd41//+peN3dly33nnHS9fTRmKE6qU6hGZ0Z0VAAAAAFBwNCIBAAAAAMFoRAIAAAAAgjEmskTQz7w8UI/lgXosD4yJLH1ci+WBeiwP1GN5YEwkAAAAAKDgaEQCAAAAAILRiAQAAAAABKMRCQAAAAAIRiMSAAAAABCMRiQAAAAAIBiNSAAAAABAMBqRAAAAAIBgNCIBAAAAAMFMFEXVXQYAAAAAQIngSSQAAAAAIBiNSAAAAABAMBqRAAAAAIBgNCIBAAAAAMFoRAIAAAAAgtGIBAAAAAAEoxEJAAAAAAhGIxIAAAAAEIxGJAAAAAAgGI1IAAAAAEAwGpEAAAAAgGA0IgEAAAAAwWhEAgAAAACC0YgEAAAAAASjEQkAAAAACEYjEgAAAAAQjEYkAAAAACAYjUgAAAAAQDAakQAAAACAYDQiAQAAAADBaEQCAAAAAILRiAQAAAAABKMRCQAAAAAIRiMSAAAAABCMRiQAAAAAIBiNSAAAAABAMBqRAAAAAIBgNCIBAAAAAMFoRAIAAAAAgtGIBAAAAAAEoxEJAAAAAAhGIxIAAAAAEIxGJAAAAAAgGI1IAAAAAEAwGpEAAAAAgGA0IgEAAAAAwWhEAgAAAACC0YgEAAAAAASjEQkAAAAACEYjEgAAAAAQjEYkAAAAACAYjUgAAAAAQDAakZVkjJlojNkziS8yxvy7usuE3FGP5YF6LA/UY3mgHssD9VgeqMfyYIyJjDEbJ/EAY8wl1VWWrI1IY8w8599SY8xCZ/voqiqkifU1xkw2xswxxgwxxqwWuG/r5Be+rNwTjTF9ilHOKIqujqLoxIAyDTbG9F9Bng7GmDHGmLnGmM+MMR3zLVcNqseLUmVZmJRnjYB9S64ejTHrpc53XnIO5+RTLuoxN9TjCsvBfbU87qstjDHPGWOmJXXSOod9S7UerzTG/NcY84cx5vLKlKsG1SP3Ve6rJVePSZ6yu6+mynSfcRpuAfk7J2Wfl/xOvjHGHF+MskVR9Pcoiq4MKNNIY8wK6zvJe1xyvivMn7URGUVRo2X/JE2W1NX52SPOC9YNKVglHCfpWEm7SGopqYGkO3I8RpPkPI6SdKkxZp90hio4jyDGmKaSnpd0g6Qmkq6X9Lwx5i/5HK+m1GNy03LLcp2kkVEU/ZzDYUqmHqMompw63y0lLZX0dJ7Hox6rQbnWo7ivlsV9VfHf4iuSDq3EMUqmHhMTJJ0v6cXKHqim1CP3Ve6rjpKpxzK+ry57nY6SNspj12nJOawm6QJJA40xbSs4fo2ox2WSertI0hdBO0RRFPRP0kRJeyZxZ0nfK/7FzJD0kKSekt5J7RNJ2jiJ60u6UfEfxQ+SBkhqEPjaT0k6z9nuIGmRpIYB+7ZOylHX+dkHks7NcB4rSeoj6VtJMyU9Iamps++xkiYlaX1Tv5fLJT3s5O0o6T1JsyRNSX5HJ0v6XdJiSfMkPV9BmQ+Q9EXqZ+Mk9Qqtr5pYj6ljGknfSeoRmL/k6rGCc7hM0ojK1iH1SD0Woh7FfVUqo/uqpLrJMVvnsE/J1WOq/A9LurwQ12JNqcfkONxXS7QexX1VKpP7quJ76ieStnKPG7BfZ0nfp372k6TDkjK/K+mWpF76r6icks6TNF3SNEknpM5xsKT+Tt5ukj6VNCf5u9hH0lWSliR/h/Mk3Zml7AMknSpppKQTV3SulRkTubakppLWV/yHtiLXSmojqb2kjSWtI+nSZYnGmFkreARuUnF9SZvkUuCkm8EukjZX/Ich/fk8zpB0kKRdFX+L9Kuku5L920q6R/GF2VJSM0nrZnit9SW9rPgbqOaKz/vTKIr+JekRSddH8TcrXQPOd9n2Frmcb6CqrsdlOklaU3l861hi9WjLrPgbygdyPd9A1CP1yH2V+2reSqweqwr3Ve6r3Fdr7331bElvRVH0Wb4FNsasZIw5WPFT2v8mP95R8ZdEaylu4GUsZ/IU+lxJeyn+G9ozy2vtIOlBxY3OJpL+T9LEKIr6Snpb0ulJPZ6eZf/tFDckw1TiG4HFklZx0nsqwzcCiv+g5kvayEnbWdL/Al/7RMXfbLSWtLqk55Jj7xywb+sk7yzFF9hXks7Mch5fSdrD2W6h+JuYuoordYiTtmqy/5++2ZF0oaShGco0WM43BxWkN0vKe5SkepJ6KO7mcW9ofdXEekwdc5CkwTnkL7l6TOXtpPgboEaVrUPqkXosRD2K+2pZ3VdVuSeRJVOPqbzFfhLJfZX7ak71KO6rZXFfldRKcbf51d3jBu7bOfkdzJL0i+Ing0c6ZZ7s5M1aTkn3SbrWSWujDE8iJd0r6ZYMZRqpLE8WJdWR9KGknULyL/tXmb64P0VRtCgwb3NJDSV9FH/hJCn+xdUJ3P8+xRU6UvHFcZOkroofbYdaI4qiPyr4efo81pc01Biz1PnZEsXfGLRU/JhfkhRF0XxjzMwMr9dK8aPknEVRNNMY003x4+27JL0q6XXldr6hqrIe4x2MaSipu+LH7rkqmXpM6SHp6SiK5hXgWBWhHqlHiftqRuV+X62kkqnHKsZ9lfuqxH01ozK+r94qqV8URbNzKuFy06IoqvDJr5x60YrL2VLSR07+SVles5Wkl3IvqqS4C+tnURSNzmWnyjQio9T2fMW/CEmSMWZtJ+1nSQslbR5F0dScXyiKliruL39ZcuwukqYm/yorfR5TJJ0QRdG76YzGmOmSNnO2Gyr+FqYiUyTtEPiaf84QRaMkbZ+8Tl3Fj75vWtF+eaiyenQcrPjbmZGVOEZajazH5PgNFH8oODgkf56oR+oxtxfivlpu99ViqJH1WIW4r3Jfze2FuK+Wy311D0kdjTHXOz973xhzVhRFj+ZxPJd7Hisq53TFjcNl1sty3CnKPAnQiupxD0m7GmP2S7abStraGNM+ytD9VSrsOpFjJW1ujGlvjFlF8aNySfaiGijpFmPMmpJkjFnHGLN3yIGNMU2NMRslfcTbSrpZ8TcES5P0y40xIwt0HgMkXZX0EZcxpnnyLYsUD5g+wBjT0RizsqR+yvw7fETSnsaYw40xdY0xzYwx7ZO0HyRtmK0QxpitjTH1TDw19I2SpkRR9GrlTi1I0erR0UPSg1HyzHyZcqzHxMGKu6aMyPNc8kE9+qjHFO6r5XNfTY5ZP9msn2wvSyvHeqyXnONKkuoaY1YxxlTFk1vuqz7uqyncV8vmvtpGUjvF4xTbJz/rKmlocqzBxpjBlT2BgHI+IamnMaatib8IuCzL4QZJOt4Ys4eJx2KuY4zZNElbUT32VPylQ/vk34eSrlA8IVNGBWtERlE0TvEf6euSxkt6J5XlAsX9i0cbY+Yk+f66LNHE66l0ynD4NRQ/op2vePDvfVE84HeZVopnOyqE2xT3YR9ujJkrabTiQbCKougLSadJelTxtwO/KsMj+yiKJkvaT9I5Wt4nul2SPEhSWxMP6n02QznOV/wNxRTF/dyL+S2dVeR6lDFmHUm7Kx78m1aO9SjFHwoeSn8oKCbq0Uc9Voj7apncVxV/k72sK+DXyfYy5ViPAxWf41GKP+QsVDyBSFFxX/VxX60Q99UyuK9GUfRjFEUzlv1LfvxzFEXL7q2FrMeM5Yyi6GXFXWvfTPK8mekgURSNkXS84plfZ0sapbjLsxT/rRxmjPnVGHN7BfvOSp3vYklzVtSd11ThdVs0xphPFQ8uztTfGyWAeiwP1GN5oB7LA/VYHqjH8kA9lr7kye5YSVtFUfR7dZenOpVFIxIAAAAAUDUKOSYSAAAAAFDmaEQCAAAAAILRiAQAAAAABMtpnUhjDAMoq0kURWbFucJQj9WHeiwP1GN5KFQ9UofVh2uxPFCP5YF6LA+h9ciTSAAAAABAMBqRAAAAAIBgNCIBAAAAAMFoRAIAAAAAgtGIBAAAAAAEoxEJAAAAAAhGIxIAAAAAEIxGJAAAAAAgGI1IAAAAAEAwGpEAAAAAgGA0IgEAAAAAwWhEAgAAAACC0YgEAAAAAASrW90FAAAAVSeKIhvfcccdXtqZZ55Z1cUBgFrj5JNP9rYPOuggG3fv3t3G8+fPr6oi5Y0nkQAAAACAYDQiAQAAAADBjNutZYWZjQnPjIKKosgU6ljUY/WhHstDbavHU045xcZbb721jU888UQvnzHLfy3Z3ltGjx5t42effdZLe+ihh2w8ffr0nMuai0LVYynUoWvJkiU2njlzppfWsWNHG48bN67KypSv2nYtlivqsTxQjxVr0KCBjV9//XUvbfz48Tbu2bNnVRUpq9B65EkkAAAAACAYjUgAAAAAQLCym521WbNmNj7hhBNsfPHFF3v5VlttNRsvXbo04/FOPfVUG997772FKCIA1DjbbLONjd37niT16NHDxiuttPy7x3SX1dDhETvuuGOFsSQdfvjhNr7qqqu8tKFDhwYdH+GaNm2adRvlza3vkSNHemmbb765jfv16+elXXHFFUUtV2216qqretvt2rWz8fHHH++lbbjhhjZ275vpLuqoevXr1/e2b7/9dhun3/OuvfbaKilTMfAkEgAAAAAQjEYkAAAAACAYjUgAAAAAQLCSHxPZp08fb9udin6dddbJuJ87DjLbOJ677rrLxlOnTvXSXnjhheBy1iZt2rSx8dtvv+2lrbHGGjZ2x1b98MMPXr4FCxbYeP3118/4Wu4xso1tdX344Yfe9pVXXmnjN954w0tbuHBh0DHLkft7mjZtWsZ8L774oo0fffRRL23llVe2ccOGDTMew11yINtroXLS42323XdfG9999902dseWVzV3CZH0OCzGRBafu2yLuxQLyseaa65p42HDhtnYHQMp+Z+NJkyYUPyCQR999JG37X6eyubjjz+2ca9evby09JISKL4OHTp42+541kGDBnlppVw/PIkEAAAAAASjEQkAAAAACFaS3VkvvfRSG1922WVeWqauqW6XO0kyxth4v/32C3rdTTbZJLSItVrr1q1tnO4W59aP2/3U7eaalq27cWi3ZNe2227rbbvdedLdMY899tigY5Yjt1th+/btM+Zzr5+///3vXpo7fXy27uVut+GXXnrJS3O71N1yyy2ZC4wKNWrUyMa33Xabl9azZ8+CvtbixYtt/Omnn3ppO+ywQ87Hc+8lktS7d28bs+RS/tz3vzT37wU1z6abbmrjI444wsbp++s555xj4/SyLe7npu233z7ja11yySU2fuyxx3IvLDJy6+v666+38ffff+/lcz93pof9rL322jZu1aqVjd3PyFJpd5csJdttt52Nn3vuuYz5nnrqKW+7lIdN8SQSAAAAABCMRiQAAAAAIBiNSAAAAABAsJIZE3nUUUfZ+Kyzzgrax126oX///l7a5MmTC1Mw/Ik7RfX//vc/L22DDTao1LEnTZrkbafHTFVW165dve1tttnGxu4U2rXB+PHjbbzxxhsH7bPVVlt526HjVN3lPw499FAvbbfddrPxI488YuMff/wx6Ni13TXXXGPjQoyBdMfsuMuCSNJnn31m4+HDh3tpBx54oI132mknG//jH//w8tWtu/xtKb0szBVXXGFjxkTmz70uQ69RVI/u3bt72zfeeKON3XF16XGuu+66q4032mijoNdyl9aS/PHpoUtoYbnVV1/dxjfccIOXts8++9h43XXXtfGvv/7q5Tv33HNtvOOOO3pphx9+eIWvG1rfqLwGDRrY2H1/So8tdz9PuXGp40kkAAAAACAYjUgAAAAAQLAa1Z3V7cY0dOhQLy3TMhxz5szxto855hgbu8t6PPvss16+Fi1a2Dhbd5558+bZmOUFwsycOdPG++67r5e2yiqr2HillZZ/hxHaVcY9tiQ1b9484zE6dOhg43S3u0zSXRDSS5TUJnvuuaeNr7vuOi/NnVq+2Nzp6d1ukNmm0MZyW265ZaWPceGFF9p40KBBNk5fj9m493Q3TndfrmyXd1SO20Uy3Z043d0RxeEOH3CXf5CyL5XkyqdLY/o+P3bs2JyPgeUOOOAAGx900EFemrus2YgRI2zsDsOSpG7dutk4U/dVVJ3GjRt72w888ICN9957bxvPmjXLy9elSxcbT5w40Uv7y1/+YuP11lvPxqVw/fEkEgAAAAAQjEYkAAAAACBYjerO6nZxSneDzNTlND1b1bhx42zsdn3bY489Mh4vfWy3i1Z6VlfkZsKECUU9/vTp0zOmtWzZMufjffnll972qFGjcj5GuXBn4Tz66KO9NHf7pJNOsnG6O3AmJ554orf9008/2bhTp04Z93PvEXXq1Al6LVSeOytuLl1YUXp23nlnG7tdySW6sxZS+nd755132jjbcAG3Dtzup+khG+6skemZxd2ulO5soHzeKaxzzjnHxquttlrGfO+9956N0937e/fuXfiCISduF9b777/fS3NnHXelr6V0F1bXaaedZuPLL7/cxulZ8bMdo7rwJBIAAAAAEIxGJAAAAAAgGI1IAAAAAECwah0T2aZNG2/bXcYjPU7RHZ/ljqdyx0Dm6/333/e2+/bta+MZM2ZU+vioGvXq1fO23WUJjDEZ93OXGnnzzTe9tMWLFxeodOVr4MCBOe+TXi7npptusnHHjh0z7pdtDCyK59tvv7WxW3f33HOPl2/y5MlVVibkL9v9EMWz5ppr2njYsGFe2g477GBj9/NPepz+ZZddZuP0Umiuzp072zg9Hu/HH3+0sbuEBArrhRdesHH79u0z5nM/cxbCyy+/XNDj1XYbbrihjdNLtbj69OljY/czTVp6vo70HBHLHHbYYd72jTfemK2Y1YInkQAAAACAYDQiAQAAAADBqrw7q9uF1Z2eekXc6atff/31oH1Gjx5d4etK/vIA6SnrW7VqZePXXnvNxukuQKeccoqNX3zxxaAyoXh23XVXb9tdKiLTEjGSNH78eBvfeuutBS8XVuzII48MyscU9Ln79NNPbZxt+ZRs3K7i559/vo179erl5XOnP//kk0+8tCFDhtjYnbo8dFkYSbr66quD8yKzbEtcoXDcLqWSdP3119t4m2228dKWLFli4//85z82vvjii718octOuddpeqiH20V2zJgxQcdD7iZNmmTjdJfVq666ysazZ8+2cXrIxgYbbGDj+vXrB73uF198kVM58WerrrqqjW+//faM+dwurDfffHPQsXv06OFtr7vuujmWrubgSSQAAAAAIBiNSAAAAABAsCrvztquXTsbH3jggV6a2130888/99IeeuihSr1uLrM67rLLLjZ2HzOnu7NedNFFNqY7a/VwZzxzu9Llwq3HiRMnVrJEyMfaa69t43T3OncGZrdLJMK43ajc7vmS9Nxzz1Xq2M2aNfO2zz33XBun63Hbbbe1sdu9rnnz5hmP/91333nbDz/8cF7lhM+9jo444ohqLEl5Sw+xSHdhdbmziWeb2TETdxjOil5r+PDhOR8fuRs0aJCN11hjDS/t7bfftrE7W256xYGPPvrIxltvvXXG17r00ktt7A7/Qn5GjBhhY/e9K82tH3f25bR9993XxieddJKXVsqzZfMkEgAAAAAQjEYkAAAAACAYjUgAAAAAQLAqHxO5//772zg9Zmbx4sU2/sc//uGl5TKmMUTr1q1tnJ6mvnfv3jbONv35tGnTClomrNjmm2/ubb/66qs2To/PysSdklmSnnrqqcoXDJWy0krLv8/6/vvvvTR3TJ87FTrCzJ8/38buOA9J2nvvvW3cpUsXG6eXXFlnnXVyft30OI9//vOfOR9j0aJF3vYvv/yS8zHwZzNmzKjuItQK6bFpX331lY3T8z64y27kI70MgTsG79dff/XS7rnnnkq9FnL3888/e9vvvPNOhfkuv/xybzvbOEh3zLi7/NHSpUvzKGHtll52wx0Hma0dkJ5nYJn0+1+2Y7hpP/30k41feumljPvUFDyJBAAAAAAEoxEJAAAAAAhW5d1ZN9lkk4xp7jTH6W5X+XCnvN5pp528tFtvvdXGa621Vl7Hf/zxx/PaD7np3r27jQcOHOilNW7c2Mbp7gKTJk2y8ZVXXmnjBx98sNBFRI5atmzpbbvdb1q0aOGlFbore222YMECb/v111+vMHanppf8Lt/uNObpaesLbc6cOUU9Pkp7evmazu2aJklPPPFEpY+56qqr2viCCy6w8UEHHeTlc++p/fr189K4rmoWdziHO3xjRZ5++mkb04W1cq655pq89nv//fdt7LZb0t3VH3300YzHcN9vb7jhBhtPmDAhrzJVJZ5EAgAAAACC0YgEAAAAAASjEQkAAAAACFblYyI//vhjG++4445emju+7eyzz/bSxo8fb2N3zFSjRo28fNtss42N27VrZ+O2bdt6+dxxINmm3nXNmzcvY5lQWO3bt7fxnXfeaWP3b0TyxxK4S8RI/vTqgwcPLmwBUSnPPPNMxrSZM2d62+mp0VF833zzjbe95ZZb2ni99dazcbdu3bx8Rx11lI232247L61OnTo5l8Odth7FEfr+h+qxyiqreNvue9nBBx9s4/SYuClTptj4gQceKE7hUBDnn3++jbPdJ9OfObk/Vo13333XxjfeeKOX5s4lsHDhQhuvvfbaGY+Xnuehf//+Nnav21LAk0gAAAAAQDAakQAAAACAYFXenfXVV1+18dFHH+2lNWnSxMbpR8aZpKcnd7vmuEuGTJ061cvnPj5+8cUXvTT30bXbdeu5557z8o0dOzaojFixBg0aeNt33XWXjZs1a2bjdNcrtwvrxRdf7KXddNNNhSwiKqlNmzY2zrbUz5AhQ7ztUpjmujaZPHmyje+44w4v7fvvv7fxueee66Wll1nK5L777rMxdY/a7o8//vC23SU+sundu7eNWdKj5nE/15x88slB+7ifiyRp9uzZBS1TbTZgwABve/vtt7exu8zcokWLgo6XXibLdfrpp3vbpdaF1cWTSAAAAABAMBqRAAAAAIBgVd6d9YUXXrBxunvT/fffb+P0TGOZ3HbbbRnTRowYYeP0jI8ud0ZBye/64XafvPLKK4PKhDD169e38e233+6lpWfuzaRLly42HjVqVGEKhqJwZ0t2u65L/vU+fPjwqioS8tCqVSsbn3jiiV5anz59bFy3bn5vLwMHDrRxepZYVC13tnPJ766MqnHkkUd62+57nmvMmDHetjsTPmqerbfe2satW7fOmG/SpEk2Tg+pQuH069ev0sc45ZRTbLzffvt5ae5nnAULFlT6tWoKnkQCAAAAAILRiAQAAAAABKMRCQAAAAAIVuVjIl3jxo3ztnfZZZcqe+2GDRvaOD3d7uabb25jt4zp8qJyOnToYOPjjz8+aJ8HHnjA22YcZGlKL9Uybdo0G6eX3EHlNG3a1Ma77bZbXsfo27evjddaay0br7322nkd77vvvrPxQw895KV99tlneR0T4c4++2wbp69FV+PGjauiOMiiW7duQfkuuOACb/unn34qRnGQpwMOOMDbfuSRR2zsLlWXvh6PPfZYG0+cOLE4hUNBPP300za+8847vTR3HOTcuXOrrEzFxpNIAAAAAEAwGpEAAAAAgGDV2p21OjVr1szG2ZaToGtdYW233XY2Dp2u+ssvv7TxhRdeWPAyoXjc6+zUU0+txpKUtzPPPNPG6SWLGjVqZOO2bdtWWZnSJk+ebOO7777bxrfcckt1FKdW++ijj2zsLjWQlp6m3u2Ch+LZc889bZyuA7fro+utt94qaplQOQcddJC37XYVd7uwzp4928vnDvVAzXbcccdlTBs/fryNR48eXRXFqRI8iQQAAAAABKMRCQAAAAAIRiMSAAAAABCs1o6JHDp0qI0zjTFYURpy17lzZxu7Y7WWLl2acR93zNSPP/5YlHKhODbZZBMbd+rUqRpLUn4OPfRQG1933XU2XnnllaujOJKkP/74w8Y33nijl/bggw/a+JtvvqmyMuHPOnbsaOObb77ZS+vdu7eNjzjiCC/t6KOPLm7BIEm66KKLbFy/fn0vzR0/N2zYsCorE3LnzuHgLtWR5n7OdN8zJennn38ufMGAAuFJJAAAAAAgGI1IAAAAAECwWtOdtWXLlt62O6252z1EkmbOnGljt9srKm/fffe1sduFNV0H8+bNs/Hbb79d/IKhKNwlXbJ1DWd6+ty5XUIXL15s42J0Z/3ll19svGTJEhsPHDjQy/fZZ5/Z+Mknnyx4OVAYv/32m40feughL83tzpquXxRP+/btbbzzzjtnzDdp0iQb/+Mf/yhiiVBZW265pY3r1auXMV+2JT5QOj744IO80koZTyIBAAAAAMFoRAIAAAAAgtWa7qzpWeayuffee2387rvvFqM4tdZmm20WlM/tnnfnnXfauE+fPl6+Tz/9tCDlQnFMnTrVxukuy64rr7yyKopTVj7//HMbP/PMMzY+7rjj8jreq6++auM333zTSxswYICN3a7mKH2jR4/2tuvWrTUfC2qUXr162Thb10f3M8mUKVOKWibkbvPNN7dx165dg/Z55ZVXbOwOF0BpGTVqlI3r1KlTjSWpOjyJBAAAAAAEoxEJAAAAAAhGIxIAAAAAEMxkG6f0p8zGhGeuYRo3buxtz5o1y8bTpk3z0jp27Ghjdzrt6hRFUeb1EXJUnfX4+uuv23i33XazcejfodvnXJL22GOPwhSsipRLPYZyxzpeeOGFGfOV2jis2laP5apQ9UgdVp9yuRbbtm1rY3eM3IQJE7x8Bx10kI3nzJlT9HJVlXKpx6222srGp59+esZ87mdQ931y7ty5RSlXVSmXeqztQuuRJ5EAAAAAgGA0IgEAAAAAwWpNd9ZSVy5dBHbddVcbX3/99TbedtttvXxPPfWUjd2uPe7PpdJbbqBc6jHUTjvtZON33nnHxgMHDvTynXLKKVVWpkKobfVYrujOWvq4FssD9VgeqMfyQHdWAAAAAEDB0YgEAAAAAASjEQkAAAAACMaYyBJBP/PyQD2WB+qxPDAmsvRxLZYH6rE8UI/lgTGRAAAAAICCoxEJAAAAAAiWU3dWAAAAAEDtxpNIAAAAAEAwGpEAAAAAgGA0IgEAAAAAwWhEAgAAAACC0YgEAAAAAASjEQkAAAAACEYjEgAAAAAQjEYkAAAAACAYjUgAAAAAQDAakQAAAACAYDQiAQAAAADBaEQCAAAAAILRiAQAAAAABKMRCQAAAAAIRiMSAAAAABCMRiQAAAAAIBiNSAAAAABAMBqRAAAAAIBgNCIBAAAAAMFoRAIAAAAAgtGIBAAAAAAEoxEJAAAAAAhGIxIAAAAAEIxGJAAAAAAgGI1IAAAAAEAwGpEAAAAAgGA0IgEAAAAAwWhEAgAAAACC0YgEAAAAAASjEQkAAAAACEYjEgAAAAAQjEYkAAAAACAYjUgAAAAAQDAakQAAAACAYDQiAQAAAADBaEQCAAAAAILRiAQAAAAABKMRCQAAAAAIRiMSAAAAABCMRiQAAAAAIBiNSAAAAABAMBqRAAAAAIBgNCIryRgz0RizZxJfZIz5d3WXCbkzxkTGmI2TeIAx5pLqLhNyx/VYHqjH8sB9tTwYY0YaY05M4qONMcOru0zIHffV8lCT6jFrI9IYM8/5t9QYs9DZPrqqCmmMaWGMec4YMy15U2qdw76tk32WlXuiMaZPMcoZRdHVURSdGFCmwcaY/ivIc6Ux5r/GmD+MMZdXplw1pR6TsvzNGDPJGDPfGPOsMaZp4H6dk7LPM8bMNcZ8Y4w5vhhljKLo71EUXRlQJvvGmiVPe2PMR8aYBcn/7fMtV02pR2PMbsnf5ixjzExjzFBjzDqB+5bq9TjCGPOTMWaOMWasMaZbvuWqQfW4vzHmnaQeZxhj/m2MaRy4b8nVozFmTWPMY8n7yGxjzLvGmB3zLVdNqcdUme4zTsMtIH9J3ledvMcl5xuUP8Mxakw9GmPOMMb8L7nPfGiM6ZjDvlHyvjrPGDPVGHOzMaZOocsYRdEjURR1CSjP5caYh1eQp7Ux5iVjzK/JPehOY0zdfMpVU+rRxB/q3bIsTMqzRsC+pXhfXS91vvOSczgnn3LVoHo0xpi+xpjJyfU4xBizWuC+JVePSZ4OxpgxyXvBZyH3n6yNyCiKGi37J2mypK7Ozx5xXjiviz4HSyW9IunQShyjSXIeR0m61BizTzpDFZxHLiZIOl/Si5U9UE2pR2PM5pLulXSspLUkLZB0dw6HmJacw2qSLpA00BjTtoLXqRH1aIxZWdIwSQ9L+oukByQNS36es5pSj5K+lLR3FEVNJLWUNF7SPTkeo9Sux7MktYiiaDVJJ0t62BjTIp8D1aB6XF1Sf8V1uJmkdSTdkOMxSqkeG0n6QNK2kpoqvh5fNMY0yudgNagel71OR0kb5bFrSd1XlzHG/EXSRZK+qMxxako9mvgLjWslHab42hwkaWiODcF2yXnsIelvkk6q4HVqUj3eLelHSS0ktZe0q6RT8zlQTanH5EO9W5brJI2MoujnHA5TMvfVKIomp853S8Wf2Z/O83g1oh4lHaf4s+ouit8jG0i6I8djlEw9mviBzvOKPwM0kXS9pOeT+2xmURQF/ZM0UdKeSdxZ0veK33BmSHpIUk9J76T2iSRtnMT1Jd2o+I/iB0kDJDUIff3kGHWTY7bOYZ/WyT51nZ99IOncDOexkqQ+kr6VNFPSE5KaOvseK2lSktY39Xu5XNLDTt6Okt6TNEvSlOR3dLKk3yUtljRP0vMrKP/Dki7P5fdUU+tR0tWSHnW2N0p+D40D9u0s6fvUz35S/IbbU9K7km5J6qX/isop6TxJ0yVNk3RC6hwHS+rv5O0m6VNJc5K/i30kXSVpiaRFST3eWUGZu0iaKsk4P5ssaZ9SrsfUMetLukbSl7XhekyOs0NS7zuUSz0mxzpE0n9rSz0mx5ojadtSr0fF742fSNrKPW7Afp1VYvdVZ/8BihscIyWdWNk6rO56lHSEpDHO9qrJsVsE7u/Vu6QnJd2p5ddqr6RcbyXpJ0j6StKvkl6VtL6z716SvpY0OznGqGW/4/TvQNLmkl6T9EtyzhcldblY8TU5T9LYDGX+StJ+zvYNku4t5XpMHdNI+k5Sj8D8y+qqZO+rki6TNKIMrsenJJ3nbHdQfF9qWI71KOkASV+kfjZOUq9s51qZMZFrK/42d/2kgCtyraQ2ir9t2ljxt96XLks0cZeq4K4b+UgeT++i+Kb3SfLj9HmcIekgxd+ItVR8g70r2b+t4icuxyZpzSStm+G11pf0suJvLporPu9Poyj6l6RHJF0fxd+sdC30eeaoKutxc0ljl21EUfSt4j/qNrkU2BizkjHmYMXflvw3+fGOim/Wayn+IJKxnMm3QecqfqPcRNKeWV5rB0kPKv5w1ETS/0maGEVRX0lvSzo9qcfTM5zvZ1FyNSY+S35eaFV6PSZdWGZJWqj4d3l9rgUutevRGPOCMWaRpP8o/uD6Ya7nHKA676v/pzye6pRaPTrHai9pZcW9PgqtquvxbMWNg8/yLXAJ3VeX7b+d4g+FxVSV9fiypDrGmB2Tp48nKG5kz8i10Mk11UnLr0cpvvY2k7S3ibvjX6T4i6Pmin/njyX7riHpGUkXS1pD8QfbXTK8TmNJryvuKdYyOec3oih6RfGXxo8n9dguQ1FvlXSkMaahiYdE7Jscq9Cq677aSdKayuOpXCneV40xRvETvAdyPd9AVV2PJhXXV3xvC1Zi9Wgq2N4i6wlW4huBxZJWcdJ7KsM3AklB5kvayEnbWdL/cvxWojJPImcprpivJJ2Z5Ty+krSHs91CcQu+ruI/viFO2qrJ/n/6RkDShZKGZijTYDnfyK6g/MV+Elll9SjpDUl/T/1sqqTOAft2VtxFYpbibzw/lXSkU+bJTt6s5ZR0n6RrnbQ2yvCNueLut7dkKNNIZfkGXNIl7t9L8rNHClGf1VmPqWM2VfyN2k6B+VurtK/Heoo/6PyzsnVYw+pxr6Q+2tSSelxNcUPpwlKvR0mtFDeEV3ePG7hvZ5XefbWO4i9wdgrJX0L1aBQ37H6X9IeknyVtn0PZI8VPdX9V3PDrr/gJR+skbUMn78tynjAk+RYo/lB7nKTRqXJ9rwqeRCrupvdJhvJcLucJSYY8m0n6KDnfKPkbMaHnXBPrMXXMQZIG55B/WV3NUmneVzspftLVqLJ1WN31KOlExU/iWivuXv5ccuydy7EeFTdOZym+putJ6qH4vSFrz4DK9MX9KYqiRYF5m0tqKOmj+IsKSXEFF3zQdxZrRFH0RwU/T5/H+orHISx1frZE8TexLRU/HpYkRVE03xgzM8PrtVJ8I6/pqrIe5yn+8OZaTdLcwP2nRVFU4TcwcuoloJwtFb9xLTMpy2u2kvRSYPnSKnu+uaiW6zGKol+MMQ9IGmuMWSfDNVaRkrweoyj6XdLLxpizjDEToih6rrLHTKnyejTG7CTpUUmHRVE0Lpd9VYL1aIxpoHjsx+goiq6pzLGyqMp6vFVSvyiKZudUwuVK7b56quIeHqPz3D8XVVmPvSQdr/iJxQTFwyFeMMZsHUXRtMBjbBNFkfdk3SmLW5frS7rNGHOTm1Xxk5r09RgZY9x9XXlfj8aYlRQ/dfyX4q6CjRR/EXGd4vkgCqk67qsNJXVX3G07VyV3X030kPR0FEXzCnCsilRlPd6n+PcyUnFj7iZJXRV/oRKqZOoxiqKZSQ+FGxU/BX1VcS+DrOdbme6sUWp7vuIKkyQZY9Z20n5W3O1t8yiKmiT/Vo/iAafVLX0eUyTt65SzSRRFq0RRNFXxWI9WyzImN4lmGY47RZknOUi/ZnWqynr8QlI759gbKu4ekOsH14q457Gicnr1KGm9LMetTD1+IWkr49zBFI9ZqtREEIFlqcrrsa7iLjtBM5etQKlcj3WzHK8yqrQejTFbK/6G9YQoit6oRLnTamQ9GmPqS3pW8Rtj7xXlr4SqrMc9JN1g4tktl3V9fN8Y87d8C++oiffVPSQd7JxvB0k3GWPuXMF++ajKemwv6YUoisZFUbQ0iruETld8foXgnssUSb1T12ODKIre05+vRyO/XpU6zoYBr1eRpor/Ru6Moui3KIpmSrpf0n4B55Kr6nh/PFjx0/2ReZQ3kxp5X02O30Bxo7lYXVkrKkvR6jG5Bi+Loqh18iXbF4p7zk2t3CnEh09t14h6jKJoVBRF20dR1FRx99lNJY3Jtk8h14kcK2lzEy9psIriR6zLCrZU0kBJtxhj1pQkY8w6xpi9Qw+eHLN+slk/2V6WdrkxZmTlT0FSPMbiqqRvsYwxzc3y6fyfknSAMaajiWfY7KfMv8NHJO1pjDncGFPXGNPMLF/e4QdlvvEqed16yTmuJKmuMWYVU4TpuitQzHp8RFJXY0wnY8yqin9/z0RRNDc51mBjzODKnkBAOZ+Q1NMY0za5IC/LcrhBko43xuxh4jFD6xhjNk3SVlSPIxV/m3SmMaa+MWbZ+J438zuznBStHo0xhxhj/pr8PppLullxl6ZfkvSyuh6NMZsaY/Y1xjRIrstjFI/hGlX501uhYtbjFoqfBJwRRdHzFaSXWz3WS15roeKJLpZmylsExbyvtlH85Vz75J8Uf2M+NDlWud1XeyruBtk++fehpCsUTzhRbMWsxw8k7W+M2dDE9lJct58nx+ppjJlYoPMYIOlCE8+YLmPM6saY7knai4rP8RATzxx5puIxXBV5QVILY8w/kve4xmb5sjk/SGpt4ieOfxLFs5X+T9IpybXcRPGTrLzH9eagqJ9XEz0kPRhFkffhvdzuq46DFXfdHJHnueSjmO+PTY0xGyXXYlvFn3P6LXvfKMd6NMZsnXzGWU3xE8kpURS9mm2fgjUio7gbVD/Fjz/HS3onleUCxV00Rhtj5iT5/uoUfp4xplOWl1iouHugFM8attBJa6V4FrlCuE3xN/PDjTFzJY1WPLmAoij6QtJpirt+TVd8wVT4qDeKosmKv1E7R8vHmix7CjdIUlsTD+p9NkM5Bio+x6MUvzkuVPzNQFEVsx6T39/fFf+h/yipsfzpvAtZjxnLGUXRy4q7gL2Z5MnYqIuiaIziLka3KJ6pbpTirgdS/LdymInXuLq9gn0XKx4sfZzivuYnSDoo+XlRFfl6XEdx42Ou4nFlSxW/gSxTbtejUfzm9KPimSvPknREFEUfV/rsVqDI9XiO4i4/g8zy9azcp+TlVo8dFM9A10XSLOecs73vFESR76s/RlE0Y9m/5Mc/R1G07D2y3O6rs1Lnu1jSnCj/7rzBinw9PihpiOIvH+dIul3x08Kvk/SC1WMURUMVdxsdkpTzc8VjvZc17rornpRkpuKJRCp83eQL4L0Uf2kxQ/HvZLck+cnk/5nGmEz3ykMUz+T6k+Lf2++KJ4kqqmJ/XjXxJEG7K67TtHK7ry7TQ9JD6UZzMRW5HtdQ3OV+vuIxxPdF8QQ1y5RjPZ6v+AnuFMXjMg/OkM8yVVjfRWOM+VTxoNRM/YRRwyXfsIyVtFUUjztDieJ6LA/UY+njvlo+jDHDJZ0VRdFX1V0W5I/7anmgHmNl0YgEAAAAAFSNQo6JBAAAAACUORqRAAAAAIBgNCIBAAAAAMFoRAIAAAAAgtXNJbMxhll4qkkURWbFucJQj9WHeiwP1GN5KFQ9UofVh2uxPFCP5YF6LA+h9ciTSAAAAABAMBqRAAAAAIBgNCIBAAAAAMFoRAIAAAAAgtGIBAAAAAAEoxEJAAAAAAhGIxIAAAAAEIxGJAAAAAAgGI1IAAAAAECwutVdAABA9WjQoIGNDzjgABt36dLFy9erV68K9zfGeNtRFNn4nXfe8dLee+89Gz/77LM2Hj16dHiBAQBAjcCTSAAAAABAMBqRAAAAAIBgNCIBAAAAAMGMO4ZlhZmNCc9cw5x99tne9s0332zjYcOGeWk33nijjdPjeqpLFEVmxbnClHI9pjVr1szGffr08dLOOeccG9911102fuaZZ7x8I0aMKFLp/ox6LA/lUo9vv/22jTt06BC0z6xZs2zcpEmTvF73l19+sfFtt93mpbn330WLFuV1/FCFqkeuxepTLteiq1WrVjbu2LGjl7b77rtXuE963LI77njmzJlBr/vQQw9522+99VbQfoVQjvVYG1GP5SG0HnkSCQAAAAAIRiMSAAAAABCsrLuzdu7c2cavvPKKl1avXr2M+y1YsMDGLVq0sPG8efMKV7gc0UUgtssuu3jbDz74oI1bt24ddAy3O54k7bzzzjYeN25c3mULQT2uWOPGjb3tE0880ca77rqrjbt27erlu+iii2x83XXXFal0sXKpx7lz59rYvSe+8cYbXr5///vfNh4/fryNN9lkEy/f9ttvb+Pzzz/fS0svB5LJvffea+N//vOfXlqhu7fW1O6sgwYN8rZ79uyZMe9LL71k4/Q1UVXq1vVXC3P/lhYuXFjU1y7Va/Evf/mLt923b18bH3vssTZu3rx50PHSv+elS5cG7efWXZ06dby00047zcYDBw4MOl6+SrUe4aMeywPdWQEAAAAABUcjEgAAAAAQrO6Ks5Qut0tNtu6raQ0bNrRxaBcsFM9uu+1m45dfftlLW3nllW0c2jU7PaPkLbfcYuPDDjvMSyt2V6xys9NOO3nb7iyD7rV08cUXe/lWX311G6+0kv/dltul3D1Gur4vu+wyGx988MFZy4VYv379bPzee+/Z+N133w3a//PPP/e2hw4dauNXX33VSzvrrLNs3K1bt4zH7N27t42nTZvmpfXv3z+oXKUuPXQi272tbdu2Nl533XVt/P333xe+YI6NNtrIxu6MupK02Wab2Tj9d3DJJZfYeM6cOUUqXc33wQcfeNsbbrhhhfnGjh3rbd966602Xrx4sY0ff/xxL19od1b3/uoOAZKqdnZWoKZy77GHHnpo0D577LGHt+0OEfnxxx+9NHcIR6nhSSQAAAAAIBiNSAAAAABAMBqRAAAAAIBgZb3ER69evWz8r3/9K3g/d9kId3mBJUuWFKZgeaht0ya7y3UMHz7cxu44HMkfP5dtDMjMmTNt3KxZs4z50mN0DjnkEBu748TcsSi5qGn1uMUWW9j41FNP9dLatWvnvpaXlum+kV7yoWnTphUeI5f7jiv0GFOmTPG2N9hgg7xeL5OaVo+lwB2v596P995774z7jBgxwtveb7/9bJzvNeiqqUt8bLzxxt72119/HbTfdtttZ+NPP/20kEX6k2uuucbG5513XvB+66yzjo1/+OGHSpejVK/FCRMmeNvumMhRo0bZ+KCDDvLyzZ49u6jlqi6lWo+hTjrpJG+7UaNGOR9jq6228rbdJa/cz63uuGNJevvtt218ww03eGkvvvhizuXIplzqcfPNN7fx66+/buO11lor4z75fsaZPn26jbt06WLjL774IvgYhcYSHwAAAACAgqMRCQAAAAAIVtZLfJx22ml57Td58mQbV2cX1tpklVVW8bZfe+01G2ea+lySFi1aZOPnnnvOS3OXG3jhhRdsfOedd3r5dt55Zxunu5G53RiOOeYYGz/22GMZy1TT7bvvvjZ+4IEHbOx2PU0L7c5anX777Tcb59J9HVXDXXLi6quvtnG27qzu8j6StOqqq9q4EN1Zy022rlaF4C5/tcsuuwTt88svv3jbf/zxR0HLVKruv/9+b/vKK6+0sdtNMb3s1KBBg4pbMKyQOyTGHfIi+ctXue+b6WuzTp06Qa8V2kXSfd10vk6dOtl422239dLcv7WPP/44qEzlyB3aI/mfQUPvq/l+LnKX2Xn66adtvOmmm+Z1vKrEk0gAAAAAQDAakQAAAACAYDQiAQAAAADBynpMZKj0GI3nn3++mkpSe11++eXedrZxkK5TTjnFxoMHDw7ap2fPnt62O77BHZciSb1797axO5ZyxowZXr70UgQ12YABA2ycbRxkqXHHc7jLD6DmmTdvXnUXoUY74ogj8trPvbe9+uqrBSrNcm3atLFxhw4dgvYZNmyYt+0ut1Sb3XLLLd52t27dbOwu1fLPf/7Ty+eO0580aVKly9GgQQMbr7nmml7a+uuvn3G/t956q9KvXar22msvG99zzz0Z8xViWatCc+tbkurVq1dNJalZnn32WW87dByku5zYHXfcYeP0mOfjjjvOxmeeeaaX5l5nK6+8ctDr1hQ8iQQAAAAABKMRCQAAAAAIRndW/XkZjw8//LCaSlK7uNP8n3vuuUH77LDDDt72Rx99VOlyuN2rLrvsMi/N7c7apEkTGx944IFevlLqzup2Q/rb3/4WtM9KK/nfNy1dujTn13WPkc/+6WN8/fXXXpq7BAvKw4IFC7ztmtIlrBjcbvX5Lk/13XffFao4FUovgYT8pf+2999/fxvffPPNNj766KO9fO+8846Nt956axv//PPPwa/917/+1cb9+/e38aGHHppxH/e1arszzjijuouQt/Tfnbs0Vm3To0cPG7du3TpjPnfZjauuuspLc5cETC9n5HK7r3fu3NlLc7uztmrVysbXX3+9l+/888/PePzqwpNIAAAAAEAwGpEAAAAAgGBl1531yCOPtPFWW20VtI/bPQTF5XZHPeGEE2zszmKW9uabb9p43LhxxSlYomHDhkU9fk1w8skn23jq1Kk2zjYTX7p++vXrZ+PZs2dn3O+SSy6x8UknnWTjfLslut1g012vCjFTIaqG23Uvm0ceecTbnjVrVhFKUzO4s/KlZ8kM9eSTTxaqOJKkxo0be9tnn312zsf45ptvClWcsvbTTz/Z2P09uzPiStL2229v4+HDh9vYnTE07ZVXXvG2t9hiCxu7s9MPGjTIy+cOM5k7d27G46Nio0aNsnG7du28tNVXXz3n43366afetjs0JT3jZyYvvvhi1mPWJu7nvfSQHdfFF19s43zvZ27977nnnhnz1alTx8bp++3YsWNtnH5vrC48iQQAAAAABKMRCQAAAAAIRiMSAAAAABCs7MZEumO3so2zcz3//PPFKk6tV79+fW/7hhtusHHz5s0z7ve///3PxocffriNiz0uw+37Xq4WLlxo4z59+lT6eO64qY4dO3ppoWPfQl177bU2njBhQkGPjeJq3769jf/+979XX0HKzPfff2/jbFPM52Ottdbytnfcccecj/Hss88WqDS1h7tcR3o5CXfcontN/ec//8l4vA033NDb/v33323sLvPEZ6Ewu+yyi43dpb8k/zPE22+/nfEYw4YNq3Q5dtppJxufddZZGfO54/1Cl1OrDdyxwcW2zTbb2LhBgwZB+7jjI6U/j1GvCXgSCQAAAAAIRiMSAAAAABCs7Lqz/uMf/6juIsDRuXNnbzvd3XEZd5pxSTr11FNt/Ouvvxa8XK4DDzzQxj179gzaZ/DgwcUpTIlwp8befffdbfz0008X9XU33nhjG6+77rpe2nfffVfU10bl9OrVy8YtW7YM2ufRRx8tVnHKhtvF3+2qXgjuchKoHmPGjPG2L730Uhu799t0l1XX/PnzvW13KbT0kg/ITXrZoWJ2F23VqpW3feutt9rYXTYrvWxHt27dbDxjxoyilK0UucvqZHPVVVfZ2F2aTpLmzJlT4T5HHXWUt33bbbflWLo/l6/QSzgVAk8iAQAAAADBaEQCAAAAAIKVfHfWgw8+2Nvecsstg/ZzZydzZ0JD5a222mo2drveSJlnzL3kkku87eHDhxe+YImtttrK27733nttXLeuf0ksXrzYxm5X6bFjxxancCXC/Z2lu20U02GHHWbj9LU+YMAAG99+++1VViZULD2TpzsLZDY333yzjUePHl3QMtVk7kyLudhss81svM4669g4tKtWesiBe8/u0KFDXmX66quvbFzs4Qi1zbhx43LeJ92FsTZdV6XO7fqf7s643Xbb2fjHH3+0cdeuXb1806dPL1LpStvdd99t49NOO81La9asmY0POeQQG7/xxhtePnfIhfv58YILLvDyNWrUKKhM2WbPnzlzZtAxqhJPIgEAAAAAwWhEAgAAAACC0YgEAAAAAAQr+TGR6623nrddv379oP3c/uOPPfZYQctU2/3zn/+0cXqcjzsN9VtvvWXjfKY/zsUWW2xh4/SU5muuuaaN01Pk77fffjYeOXJkcQpXgnbddVcbZxrnmrbSSsu/s1q6dGler+se469//auX1r17dxvX5jGRrVu39rYvuugiG6fHyoR66qmnbOwupfLll196+dzx5a+++qqX1rhx4wqPPWXKFG/7jjvusLE7JrncueMPQ6+pNHdcT7ap/M877zwbp98z8702XV988YWNa+I4nlKyww47eNuDBg2qMN+3337rba+99to23mijjbw0d6yV+7eAmueII46wsTsGMu23336zMWMgw7jtgK+//tpL22WXXSrc56677vK2e/fubeM6derYePPNN8+rTD/88ION77///ryOUZV4EgkAAAAACEYjEgAAAAAQrOS7s6aXZAj1xx9/FLgktZc7rbwk9erVK2i/66+/3sZuV4xCOfTQQ21866232tidMlvyu+dde+21XhpdWCvmdkt242zcbnKh++RyjG233dbG+++/v5eW7sJcDtwpyN2/2x49enj53C42+Tr11FMr/Hm6Dtxtt+txmtuN6MADD/TSJk+enE8RS97ll19u4zlz5nhpp59+uo3dek87/vjjc37ddPfVfK9N12uvvVbpY9RmHTt2tPFNN93kpbnd5NylynbeeWcvnzuU5LnnnvPSjjnmGBv/+9//tvE333yTZ4lRLKuvvrqNZ8+enTGtSZMmNnaHm0jSqFGjilO4MnL44Yd72x9//LGN11prrYz7pZeMq214EgkAAAAACEYjEgAAAAAQjEYkAAAAACCYyWX8gzGm8oMlCmzRokXedr169YL2c8cElMISH1EU5TfnewUKXY9PP/20t33QQQfZOD3e5oYbbrDxJZdcYuMlS5bk9drusgHnnHOOl3bxxRfb2J0yPz0e9swzz7Txvffem1c5QtXkesyFO8Yi01TYae+//35QvvSSA9tss42N3XrMdu969913ve30GJHKqo56TI9tdJfSSY/ldS1YsMDGocstNG3a1NteddVVg/bLx9SpU71td6kWd1yK5I8BK4RC1WOxr0V3HGR6fOpJJ51kY3e8d3pcpTsO1ZX+23HHWaXH44VylyL49NNP8zpGqHK5p7ruu+8+G/fs2dNLmz9/vo3/9re/2fj555/38rljkt944w0vzb0fuu95p5xySn4FLoByrMd8pK859z6f/jw1bdo0Gx9wwAE2Hjt2bJFKt2LlUo/uNbL99tvb+OSTT/bybbzxxgV9XfezS6dOnQp67FyE1iNPIgEAAAAAwWhEAgAAAACCleQSHzvssIONs00j7/r555+97fHjxxe0TLXNmmuuaWO3PtLSv/eLLrqoUq/bvn17b3vIkCE23mSTTTLu53ZhdbuHSMXvwlqOjj76aBsfe+yxNk53KT733HNtPHjw4IzHc7uwtmvXzkt77733ci7feuutl/M+NV2XLl287UxdWOfOnettu0t+DBs2LOi13OVxJOmJJ54I2i8f6SWC3Pq+6qqrvLR+/frZuDYt0+R2Q77yyiu9tIEDB9rYXRpi4sSJXr4PP/ww6LXS99gQ6S6r6dfGiq2yyio2Ti9R5HKX4Uh3YXW5XR8nTZqUMd8GG2wQWkQUScOGDW2c/hyTbVmrZ5991sbV2YW1HLlDdtz44Ycf9vK5n4XcperS3V632GKLoNcdMGBATuWsbjyJBAAAAAAEoxEJAAAAAAhWMt1Z3VlX3S5y6RkLM/n++++97dCuPaiYO2OmOyNg2uOPP57X8Zs0aWJjt7uWOwOZ5HeDTHf1+O6772zsdjkYM2ZMXmXCcu71NGLECBunu9q5XV3d7qzpLnPnnXeejY844ogClbK8/Oc//wnK9/XXX3vb6ZkZXa1bt7Zxr169bNy5c+ecylaRkSNHetsrr7yyjTt06BB0jL59+3rb7kyudEOPzZgxw8ZPPfVUpY+Xz0y806dP97ZnzZpV6XLUNnvuuaeNmzdvnjHfNddck/OxX331VW/7uOOOs7HbnbxRo0Zevnnz5uX8Wgjjzix/yy232Hi//fbLuM+bb77pbV944YWFLxiycu+3UuYZrA855JDgY7rva2+//XZ+BasmPIkEAAAAAASjEQkAAAAACEYjEgAAAAAQrGTGRDZo0MDG6ennUfX22WefoHw//PBDUL70OBx3KQJ32vq0xYsX2/jWW2/10tzlAdLLHqBwbr75ZhsbY7w0d2ydO2bVnbY8F+6SPtmOUWrjCkK4f+uSv4yCO7Zx++239/K9//77Nl6wYIGX5o5nzja2OZP00gHHH3+8jd966y0vzR0T6Y7/csfDSlKnTp0yvp57nigOdyw5qs78+fNtvGTJEhun532YPXt2zsdee+21M6a547EYA1l13M81PXv2zJjP/eySHn9HfZWH+++/38aTJ0+uxpLkjieRAAAAAIBgNCIBAAAAAMFKpjsrapaZM2cG5VtrrbW87S222MLG7lIOe++9t5dv2223rfB448aN87a7d+9u488//zyoTCisfv362fiFF17ImM/tfppejiVUtmPcd999Nj7rrLPyOn5Nlu665F4/2Zb/aNu2baVf2+2W7k5H/8ADD3j5fvzxx4zH+O2332z84osv2jg9bf0FF1xg43S3+TvvvDOwxMjXpptuWt1FqJXcpZJ++uknG6e7orpLfLj3hNGjR3v52rRpY+ODDz64YOVEfi699FJv+6STTgra79///reN00u1oDz897//re4i5I0nkQAAAACAYDQiAQAAAADBaEQCAAAAAIIxJhJ5GThwoI1POeUUL80dB3nGGWd4aentTNxprd2xbn379vXyLVy4MOh4KJ5Zs2ZV2WtNmzbNxr169fLS3nvvPRvXhr+LTz75xMZbbrmljdNjb9xxw2nuGNMbb7zRxu6YRUkaO3asjQu9XE66ri6//HIbX3vttV7aokWLCvra+LODDjqouotQ651wwgk2HjBggJfmzhfgLmWUXgKobt3lH+/c5XUkfzz54MGDK1VWZPbss8/auGvXrhnzuffUbt26eWmjRo0qeLlQHO51Vq9evWosSdXhSSQAAAAAIBiNSAAAAABAsJLpzupOD//GG2/YeI899si4z6233mpjtzsWKm/GjBk2TnefO/zww22crX7c5Tqef/55L+22226z8dSpU/MuJ0qDe32PHz/eS+vfv3+FabX9ml6yZImNv/zySxsfeeSRXr70dimh+ypqo1deecXG6eWujj76aBsfcsghNv6///u/jMdLL4f00Ucf2Xj48OF5lxNSw4YNve17773Xxm4X1mzLWo0cOdLGdF8tXS1btqwwLmc8iQQAAAAABKMRCQAAAAAIZrI9Yv9TZmPCM6OgoigyhToW9Vh9yrEeW7RoYeMXXnjBS2vXrp2NjVl+6v369fPyff311zZ+/PHHC13EgivHeqyNClWP5VSH9evXt/Hxxx/vpd155502njJlio332msvL9+ECROKVLo/41osD6Vajx07dvS23a6p7nte+rO2+17pzjQ+c+bMApewapVqPRbCbrvtZuPXX3/dS8v2t3DYYYfZeOjQoUUqXW5C65EnkQAAAACAYDQiAQAAAADBaEQCAAAAAIKVzBIfAGqm6dOn2zg9HT2A0uIutzNgwAAvLb0N1HaNGjXytufOnWvj1VZbLeN+X3zxhY1LfRwkYiNGjLBxz549vbQTTjjBxo899piXVlPGQeaDJ5EAAAAAgGA0IgEAAAAAwVjio0TU5mmTywn1WB6ox/LAEh+lj2uxPJRLPe666642fvPNN238ySefePm6du1qY3dISKkrl3qs7VjiAwAAAABQcDQiAQAAAADBaEQCAAAAAIIxJrJE0M+8PFCP5YF6LA+MiSx9XIvlgXosD9RjeWBMJAAAAACg4GhEAgAAAACC5dSdFQAAAABQu/EkEgAAAAAQjEYkAAAAACAYjUgAAAAAQDAakQAAAACAYDQiAQAAAADBaEQCAAAAAIL9PzZu18ZTXJ9rAAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure = plt.figure(figsize=(16, 8))\n",
    "cols, rows = 8, 3\n",
    "for i in range(1, cols * rows + 1):\n",
    "    sample_idx = torch.randint(len(test_data), size=(1,)).item()\n",
    "    img, label = test_data[sample_idx]\n",
    "\n",
    "    img_gpu = img.to(device)\n",
    "\n",
    "    y_probs = load_model(torch.unsqueeze(img_gpu, 0))\n",
    "    y_hat = torch.argmax(y_probs, 1)\n",
    "\n",
    "    figure.add_subplot(rows, cols, i)\n",
    "    plt.title(f\"True: {label}, Predict {int(y_hat)}\")\n",
    "    plt.axis(\"off\")\n",
    "    plt.imshow(img.squeeze(), cmap=\"gray\")\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Покажем изображения, на которых модель ошибается"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "false_imgs, false_labels, true_labels = compute_error_model(load_model, test_loader, device)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1152x576 with 24 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure = plt.figure(figsize=(16, 8))\n",
    "cols, rows = 8, 3\n",
    "for i in range(1, cols * rows + 1):\n",
    "    sample_idx = torch.randint(len(false_imgs), size=(1,)).item()\n",
    "    img, label_true, label_false = false_imgs[sample_idx], true_labels[sample_idx], false_labels[sample_idx]\n",
    "\n",
    "    figure.add_subplot(rows, cols, i)\n",
    "    plt.title(f\"True: {label_true}, Predict {int(label_false)}\")\n",
    "    plt.axis(\"off\")\n",
    "    plt.imshow(torch.tensor(img).squeeze(), cmap=\"gray\")\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}